{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QV63W72MXM3CMWM2OOQDVW5D3A","short_pith_number":"pith:QV63W72M","schema_version":"1.0","canonical_sha256":"857dbb7f4cbb3626599a73a03adba3d832004e41ebb84396cdc76f6e08ce999e","source":{"kind":"arxiv","id":"2006.13886","version":1},"attestation_state":"computed","paper":{"title":"Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.CV"],"primary_cat":"eess.IV","authors_text":"Anthony D. Rollett, Elizabeth A. Holm, Gregory A. Hackett, Harry W. Abernathy, Hokon Kim, Paul A. Salvador, Tim Hsu, William K. Epting","submitted_at":"2020-06-22T21:52:01Z","abstract_excerpt":"Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-bas"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2006.13886","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-06-22T21:52:01Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.CV"],"title_canon_sha256":"5f4e64e43ef94959ac9195e4ee620c0acd508326af0f1c70a5ef8a47e54b62e2","abstract_canon_sha256":"82b338d7fcbf6cf79d4c143339e58fd91d17035490d1c85b548cd6aa500d40de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:13:20.991602Z","signature_b64":"RgjcIP5qFmeFzYVoBucVyr26NN3bpWWWvqlVGO5B1jBNr8OH5xyDFFAflJymqxUk/rIlrdobaLLQHFC10f/jCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"857dbb7f4cbb3626599a73a03adba3d832004e41ebb84396cdc76f6e08ce999e","last_reissued_at":"2026-07-05T01:13:20.991202Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:13:20.991202Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.CV"],"primary_cat":"eess.IV","authors_text":"Anthony D. Rollett, Elizabeth A. Holm, Gregory A. Hackett, Harry W. Abernathy, Hokon Kim, Paul A. Salvador, Tim Hsu, William K. Epting","submitted_at":"2020-06-22T21:52:01Z","abstract_excerpt":"Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.13886","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.13886/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2006.13886","created_at":"2026-07-05T01:13:20.991258+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.13886v1","created_at":"2026-07-05T01:13:20.991258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.13886","created_at":"2026-07-05T01:13:20.991258+00:00"},{"alias_kind":"pith_short_12","alias_value":"QV63W72MXM3C","created_at":"2026-07-05T01:13:20.991258+00:00"},{"alias_kind":"pith_short_16","alias_value":"QV63W72MXM3CMWM2","created_at":"2026-07-05T01:13:20.991258+00:00"},{"alias_kind":"pith_short_8","alias_value":"QV63W72M","created_at":"2026-07-05T01:13:20.991258+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A","json":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A.json","graph_json":"https://pith.science/api/pith-number/QV63W72MXM3CMWM2OOQDVW5D3A/graph.json","events_json":"https://pith.science/api/pith-number/QV63W72MXM3CMWM2OOQDVW5D3A/events.json","paper":"https://pith.science/paper/QV63W72M"},"agent_actions":{"view_html":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A","download_json":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A.json","view_paper":"https://pith.science/paper/QV63W72M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.13886&json=true","fetch_graph":"https://pith.science/api/pith-number/QV63W72MXM3CMWM2OOQDVW5D3A/graph.json","fetch_events":"https://pith.science/api/pith-number/QV63W72MXM3CMWM2OOQDVW5D3A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A/action/storage_attestation","attest_author":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A/action/author_attestation","sign_citation":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A/action/citation_signature","submit_replication":"https://pith.science/pith/QV63W72MXM3CMWM2OOQDVW5D3A/action/replication_record"}},"created_at":"2026-07-05T01:13:20.991258+00:00","updated_at":"2026-07-05T01:13:20.991258+00:00"}